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Results for “Synthetic Biology”

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bioRxiv2021

Chaos in small microbial communities

Predictability is a fundamental requirement in biological engineering. As we move to building coordinated multicellular systems, the potential for such systems to display chaotic behaviour becomes a concern. Therefore understanding which systems show chaos is an important design consideration. We developed a methodology to explore the potential for chaotic dynamics in small microbial communities governed by resource competition, intercellular communication and competitive bacteriocin interactions. We show that we can expect to find chaotic states in relatively small synthetic microbial systems, understand the governing dynamics and provide insights into how to control such systems. This work is the first to query the existence of chaotic behaviour in synthetic microbial communities and has important ramifications for the fields of biotechnology, bioprocessing and synthetic biology.

microbiology↗

bioRxiv2017

DeepMetabolism: A Deep Learning System To Predict Phenotype From Genome Sequencing

Life science is entering a new era of petabyte-level sequencing data. Converting such \"big data\" to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.

systems biology↗

bioRxiv2026

Spatial control of light-responsive proteins and optogenetics within hydrogels via volumetric bioprinting

Spatiotemporal control over cell fate and behaviour within bioprinted constructs remains a key challenge in tissue engineering. Optogenetics offers versatile potential for non-invasive regulation of biological processes. Yet, its integration within large-scale, cell-laden bioprinted materials is still limited, especially considering spatial constraints of existing light delivery methods. In this study, we introduce a novel approach that repurposes tomographic volumetric bioprinting to enable post-printing stimulation of photosensitive protein-switches and optogenetic circuits in cells deep within hydrogel constructs. By converging different bioprinting approaches, computer vision, context-aware model generation, and synthetic biology and cell engineering, we demonstrated selective activation of a fluorescent, light-responsive protein probe within multi-material centimeter-scale constructs. Moreover, leveraging a multi-wavelength volumetric bioprinter, we further demonstrate this concept by selectively stimulating cells expressing a near-infrared optogenetic system that triggers gene expression and the induction of pancreas-specific transcription factors. The described methods provide platforms for remote, repeatable, and localized control of biological events in volumetric constructs, opening new possibilities for advanced tissue models, and dynamic tuning of cell-mediated protein production in engineered living systems.

bioengineering↗

bioRxiv2025

Novel algal modular LOV domain proteins expand opto-biotechnological avenues for controlling of eukaryotic riboswitching, translational and proteolytic processes.

Light, Oxygen, or Voltage (LOV) domains mediate blue light-gated signal transduction, regulating diverse opto-biological functions. LOV domain functions either standalone or fused with effector domains, regulating the downstream signalling process. The current repertoire of LOV domain-based tools is limited to a relatively small number of naturally occurring proteins. In this study, we have identified novel algal LOV domain fused with different effector domains as potential light-mediated translational, ribogenetic and proteolytic switches, highlighting their unexplored avenues of opto-biotechnology. LOV-domain fusion with eIF4E suggests its potential as a light-controlled translational switch and as an opto-ribogenetic regulator. LOV-SppA might be used as a light-gated proteolytic switch. Additionally, LOV coupled with UFD1, UbiH, mannosyl-oligosaccharide glucosidase and biosynthetic gene cluster (BCG) molecular chassis pave the way for opto-biomanufacturing strategies of relevant valuable algal bioactive. Here, we report the discovery of 13 novel algal modular LOV domain-containing proteins across the algal system through comprehensive bioinformatics, bio-curation and systems biology approaches. It offers important insights into the structural and functional diversity of LOV photoreceptors in algae. Hence, these newly identified modular LOV domain-containing proteins expand the platform of opto-biotechnology applications. These finding lay the foundation for future research on the mechanistic basis for light-driven signalling cascade of RNA, translational and protein homeostasis in algae, and potentiate development of next-generation opto-biotechnological tools for synthetic biology, optogenetics and opto-biomanufacturing of valuable bioactive via regulation of biosynthetic gene cluster (BGC) in green lineage.

bioinformatics↗

bioRxiv2022

Ultra-small bispecific fusion protein augments tumor penetration and treatment efficacy for pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related deaths. Due to drugs low intrinsic anticancer activity and the unique physiological barrier of PDAC tumors, the once highly anticipated antibody-based pathway-targeted therapies have not achieved promising improvement in outcomes. Here, an ultra-small-sized bispecific fusion protein, termed Bi-fp50, that could largely enrich deep tumor tissue and effectively inhibit PDAC tumor growth was reported. The bispecific Bi-fp50 protein was constructed by a typical synthetic biology method that could efficiently target EGFR and VEGF of PDAC cells simultaneously in vitro and in vivo. For Bxpc3 and Aspc1 PDAC cells, the Bi-fp50 achieved a significant and synergistic therapeutic effect. Owing to the small size of only 50 kDa and the function of reducing the interstitial fluid pressure by vascular normalization, the Bi-fp50 showed enhanced penetration, considerable accumulation, and uniform distribution in tumor and subsequently led to effective inhibition of the growth of Bxpc3 cells-induced PDAC tumor in vivo. Furthermore, no noticeable side effect of Bi-fp50 was found in vitro and in vivo. This work demonstrates that the synthetic Bi-fp50 fusion protein could be used as a new effective pathway-specific targeted therapy for PDACs.

cancer biology↗

bioRxiv2016

Pan- and core- network analysis of co-expression genes in a model plant

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of pan-network and core-network representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.\n\nResearch AreaSystems and Synthetic Biology\n\nSummaryBy analyzing 134 microarray datasets for Arabidopsis, we found that gene coexpression networks are highly context-dependent.\n\nFinancial sourceWork at Brookhaven was supported by grants PM-031 from the Office of Biological Research of the U.S. Department of Energy.

Bioinformatics↗

bioRxiv2021

MoBiFC: development of a modular bimolecular fluorescence complementation toolkit for the analysis of chloroplast protein-protein interactions.

The bimolecular fluorescence complementation (BiFC) assay has emerged as one of the most popular methods for analysing protein-protein interactions (PPIs) in plant biology. This includes its increasing use as a tool for dissecting the molecular mechanisms of chloroplast function. However, the construction of chloroplast fusion proteins for BiFC can be difficult, and the availability and selection of appropriate controls is not trivial. Furthermore, the challenges of performing BiFC in restricted cellular compartments has not been specifically addressed. Here we describe the development of a flexible modular cloning-based toolkit (MoBiFC) for chloroplast BiFC and proximity labelling using synthetic biology principles. The approach facilitates the cloning process for chloroplast-targeted proteins, allows robust ratiometric quantification, and the toolkit comes with model positive and negative controls. Our study also highlights many potential pitfalls including the choice of fluorescent protein (FP) split, negative controls, cell type, and reference FP. Finally, we provide an example of how users can enrich the toolset by providing functional proximity labelling modules, and we discuss how MoBiFC could be further improved and extended to other compartments of the plant cell.

plant biology↗

bioRxiv2025

Protein-free membrane fusion: a refined view of the delicate fusogenic properties of calcium

In the late 20th century, calcium took on the identity of an independent fusogen, when it was found to induce fusion of anionic large unilamellar vesicles (LUVs), yet its ability to drive fusion in cell-sized membranes remains poorly understood. Here, we directly quantify calcium-mediated fusion of giant unilamellar vesicles (GUVs) using a microfluidic trapping platform combined with confocal microscopy, enabling simultaneous measurement of lipid mixing, content mixing, and fusion outcomes across hundreds of single vesicles. We systematically map fusion efficiency as a function of calcium concentration, membrane composition, and mechanically imposed tension. We find that calcium-induced fusion of GUVs in the absence of proteins is remarkably fickle and composition-sensitive, as the vesicles need to be sufficiently instable to allow the opening of the fusion pore, yet stable enough to prevent bursting and collapse. Negatively charged GUVs containing high fractions of DOPE exhibit the highest fusogenic responsiveness, whereas other compositions undergo extensive lipid mixing without pore formation. Increasing membrane tension can shift this balance and promote full fusion, revealing a narrow parameter space in which calcium acts as an effective protein-free fusogen for cell-sized membranes. These findings clarify longstanding discrepancies between LUV- and GUV-based calcium fusion assays and provide quantitative design rules for employing calcium as a fusogen in synthetic biology and membrane-reconstitution studies, where controlled membrane growth, vesicle-vesicle fusion, and module integration are central to building and sustaining artificial cells. SIGNIFICANCECalcium is widely regarded as a potent, protein-free fusogen in nanometer-scale lipid vesicles, yet its relevance for fusion between cell-sized membranes remains unresolved. This gap limits our ability to translate decades of LUV-based fusion studies to more physiologically realistic systems and restricts the use of calcium in synthetic cell engineering. By directly quantifying calcium-mediated fusion in giant unilamellar vesicles (GUVs), we identify the membrane compositions and mechanical conditions under which calcium can reliably drive full fusion rather than mere lipid mixing. Our findings provide the first systematic map of calciums fusogenic parameter space in cell-sized membranes, enabling more informed design of fusion-based assays, reconstitution experiments, and strategies for membrane growth in artificial cells.

biophysics↗

bioRxiv2025

Protein structure prediction and design for high-throughput computing

Recent advances in structural biology and machines learning have resulted in a revolution in molecular biology. This revolution is driven by protein structure prediction and design tools such as Alphafold3, Chai-1, and Boltz-2 which are now able to accurately model protein structures as well as predict protein-complex formation with a variety of substrates at atomic resolution (i.e., DNA, RNA, small ligands, post-translational modifications). The impact of these protein-structure prediction algorithms has been matched by the emergence of in silico protein design platforms (RFdiffusion), which now promise to revolutionize synthetic biology and novel disease therapeutics. Despite their potential to transform molecular biology, the adoption of these algorithms is hindered in part, not only by their high computational requirements, but also by the difficulty in deploying these algorithms on available systems. To help address these barriers, we developed containerized solutions for AlphaFold3, Chai-1, Boltz-2, and RFdiffusion, optimized across a variety of computational architectures (e.g., x86 and ARM). Additionally, we present OmniFold, an optimized wrapper-platform with automatic QC report generation that enables AlphaFold3, Chai-1, and Boltz-2 to perform simultaneously while more efficiently utilizing GPU systems. Precompiled containers and their definition files are available as open source through Sylabs and GitHub. We hope that these containers and repos will help to facilitate reproducibility, accessibility, and accelerate scientific discovery. Availability and implementationSource code for containers is available at: https://github.com/EpiGenomicsCode/ProteinStruct-Containers https://github.com/EpiGenomicsCode/ProteinDesign-Containers https://github.com/EpiGenomicsCode/OmniFold

bioinformatics↗

bioRxiv2023

The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR

Transcription factor binding sites (TFBSs) are important sources of evolutionary innovations. Understanding how evolution navigates the sequence space of such sites can be achieved by mapping TFBS adaptive landscapes. In such a landscape, an individual location corresponds to a TFBS bound by a transcription factor. The elevation at that location corresponds to the strength of transcriptional regulation conveyed by the sequence. We developed an in vivo massively parallel reporter assay to map the landscape of bacterial TFBSs. We applied this assay to the TetR repressor, for which few TFBSs are known. We quantify the strength of transcriptional repression for 17,765 TFBSs and show that the resulting landscape is highly rugged, with 2,092 peaks. Only a few peaks convey stronger repression than the wild type. Non-additive (epistatic) interactions between mutations are frequent. Despite these hallmarks of ruggedness, most high peaks are evolutionarily accessible. They have large basins of attraction and are reached by around 20% of populations evolving on the landscape. Which high peak is reached during evolution is unpredictable and contingent on the mutational path taken. This first in-depth analysis of a prokaryotic gene regulator reveals a landscape that is navigable but much more rugged than the landscapes of eukaryotic regulators. SignificanceUnderstanding how evolution explores the vast space of genotypic possibilities is a fundamental question in evolutionary biology. The mapping of genotypes to quantitative traits (such as phenotypes and fitness) allows us to delineate adaptive landscapes and their topological properties, shedding light on how evolution can navigate such vast spaces. In this study, we focused on mapping a transcription factor binding site (TFBS) landscape to gene expression levels, as changes in gene expression patterns play a crucial role in biological innovation. We developed a massively parallel reporter assay and mapped the first comprehensive in vivo gene regulatory landscape for a bacterial transcriptional regulator, TetR. Surprisingly, this landscape is way more rugged than those observed in eukaryotic regulators. Despite its ruggedness, the landscape remains highly navigable through adaptive evolution. Our study presents the first high-resolution landscape for a bacterial TFBS, offering valuable insights into the evolution of TFBS in vivo. Moreover, it holds promise as a framework for discovering new genetic components for synthetic biological systems.

evolutionary biology↗

bioRxiv2017

Tellurium Notebooks - An Environment for Dynamical Model Development, Reproducibility, and Reuse

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system that facilitates building reproducible dynamical models and reusing models by 1) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 2) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.\n\nAuthor summaryThere is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human-readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human-readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard-encoded models and simulations, regardless of the users level of technical knowledge of said standards.

systems biology↗

bioRxiv2020

Systemic mutational rescue in Escherichia coli elicited by a valency dependent, high affinity protein DNA interaction

The controlled formation of sequence specific DNA protein complexes is a fundamental feature of most genetic transactions. Studies of the impact of point mutations on the function of individual components, such as repressors, remains a key aspect of many Systems and Synthetic Biology research programmes. One of the most dramatic systemic consequences of a point mutation is exhibited by the monomeric DNA methyltransferases M.HhaI and M.EcoRII, where substitution of a single, catalytic cysteine by either glycine or alanine, creates a lethal gain of function phenotype. In vivo expression of these point mutants promotes the deposition of high affinity nucleoprotein complexes that arrest replication in vivo, causing cell death. Interestingly, it appears that a systemic response to expression of these mutant enzymes is dramatically enhanced when they are expressed as synthetic dimers. A previously unreported form of "mutational rescue" appears to be triggered as a result of networked crosslinking of host cell DNA resulting from an increased valency of nucleoprotein complex formation. This finding may have significance for developing molecular interventions for the controlled regulation of genome function and for the development of novel antimicrobial and anticancer strategies.

genomics↗

bioRxiv2017

MetQy: an R package to query metabolic functions of genes and genomes

SummaryWith the rapid accumulation of sequencing data from genomic and metagenomic studies, there is an acute need for better tools that facilitate their analyses against biological functions. To this end, we developed MetQy, an open-source R package designed for query-based analysis of functional units in [meta]genomes and/or sets of genes using the The Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Furthermore, MetQy contains visualization and analysis tools and facilitates KEGGs flat file manipulation. Thus, MetQy enables better understanding of metabolic capabilities of known genomes or user-specified [meta]genomes by using the available information and can help guide studies in microbial ecology, metabolic engineering and synthetic biology.\n\nAvailability and ImplementationThe MetQy R package is freely available and can be downloaded from our groups website (http://osslab.lifesci.warwick.ac.uk) or GitHub (https://github.com/OSS-Lab/MetQy).\n\nContactO.Soyer@warwick.ac.uk

microbiology↗

bioRxiv2018

An Open-Source Plate Reader

Microplate readers are foundational instruments in experimental biology and bioengineering that enable multiplexed spectrophotometric measurements. To enhance their accessibility, we here report the design, construction, validation, and benchmarking of an open-source microplate reader. The system features full-spectrum absorbance and fluorescence emission detection, in situ optogenetic stimulation, and stand-alone touch screen programming of automated assay protocols. The total system costs <$3500, a fraction of the cost of commercial plate readers, and can detect the fluorescence of common dyes down to [~]10 nanomolar concentration. Functional capabilities were demonstrated in context of synthetic biology, optogenetics, and photosensory biology: by steady-state measurements of ligand-induced reporter gene expression in a model of bacterial quorum sensing, and by flavin photocycling kinetic measurements of a LOV (light-oxygen-voltage) domain photoreceptor used for optogenetic transcriptional activation. Fully detailed guides for assembling the device and automating it using the custom Python-based API (Application Program Interface) are provided. This work contributes a key technology to the growing community-wide infrastructure of open-source biology-focused hardware, whose creation is facilitated by rapid prototyping capabilities and low-cost electronics, optoelectronics, and microcomputers.\n\nTable of Contents Graphic\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC=\"FIGDIR/small/413781_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (30K):\norg.highwire.dtl.DTLVardef@c26eaborg.highwire.dtl.DTLVardef@efb695org.highwire.dtl.DTLVardef@1bc2f41org.highwire.dtl.DTLVardef@1c25c79_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Protein evolvability under rewired genetic codes

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability -- the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether this robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively-parallel sequence-to-function assays to construct and analyze empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we demonstrate that alternative genetic codes can radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering non-standard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the biocontainment of synthetic organisms, respectively. Our findings demonstrate that the standard genetic code, a critical and near-universal cellular information processing system, not only mitigates replication and translation errors as compared to most alternative genetic codes, but also facilitates predictable and directional adaptive evolution by enabling evolving populations to readily find mutational paths to adaptation.

evolutionary biology↗

bioRxiv2022

Programmable eukaryotic protein expression with RNA sensors

The diversity of cell types and states can be scalably measured and defined by expressed RNA transcripts. However, approaches to programmably sense and respond to the presence of specific RNAs within living biological systems with high sensitivity are lacking. RNA sensors that gate expression of reporter or cargo genes would have diverse applications for basic biology, diagnostics and therapeutics by enabling cell-state specific control of transgene expression. Here, we engineer a novel programmable RNA-sensing technology, Reprogrammable ADAR Sensors (RADARS), which leverages RNA editing by adenosine deaminases acting on RNA (ADAR) to gate translation of a protein payload on the presence of endogenous RNA transcripts. In mammalian cells, we engineer RADARS with diverse payloads, including luciferase and fluorescent proteins, with up to 164-fold activation and quantitative detection in the presence of target RNAs. We show RADARS are functional either expressed from DNA or as synthetic mRNA. Importantly, RADARS can function with endogenous cellular ADAR. We apply RADARS to multiple contexts, including RNA-sensing induced cell death via caspases, cell type identification, and in vivo control of synthetic mRNA translation, demonstrating RADARS as a tool with significant potential for gene and cell therapy, synthetic biology, and biomedical research. One Sentence SummaryA new technology utilizing ADAR mediated RNA-editing enables robust reprogrammable protein expression based on target RNA transcripts in mammalian cells, leading to broad applications in basic science research, cell engineering, and gene therapy.

genomics↗

bioRxiv2019

Electroporated recombinant proteins as tools for in vivo functional complementation, imaging, and chemical biology

Delivery of native or chemically modified recombinant proteins into mammalian cells shows promise for functional investigations and various technological applications, but concerns that sub-cellular localization and functional integrity of delivered proteins may be affected remain high. Here, we surveyed batch electroporation as a delivery tool for single polypeptides and multi-subunit protein assemblies of kinetochores, a spatially confined and well-studied subcellular structures. After electroporation in human cells, recombinant fluorescent Ndc80 and Mis12 multi-subunit complexes displayed native localization, physically interacted with endogenous binding partners, and functionally complemented depleted endogenous counterparts to promote mitotic checkpoint signaling and chromosome segregation. Farnesylation is required for kinetochore localization of the Dynein adaptor Spindly. In cells with chronically inhibited farnesyl transferase activity, in vitro farnesylation and electroporation reconstituted robust kinetochore localization of Spindly. Thus, electroporation is uniquely versatile for delivering synthetic and, as required, chemically modified functional mimics of endogenous proteins, and is therefore a promising tool for chemical and synthetic biology.

cell biology↗

bioRxiv2021

Probiotic-guided CAR-T cells for universal solid tumor targeting

Synthetic biology enables the engineering of interactions between living medicines to overcome the specific limitations of any singular therapy. One major challenge of tumor-antigen targeting therapies like chimeric antigen receptor (CAR)-T cells is the identification of targetable antigens that are specifically and uniformly expressed on heterogenous solid tumors. In contrast, certain species of bacteria selectively colonize immune-privileged tumor cores and can be readily engineered as antigen-independent platforms for therapeutic delivery. Bridging these approaches, we develop a platform of probiotic-guided CAR-T cells (ProCARs), in which T cells are engineered to sense synthetic antigens (SA) that are produced and released by tumor-colonizing probiotic bacteria. We demonstrate increased CAR-T cell activation and tumor-cell lysis when SAs anchor to components of the extracellular matrix. Moreover, we show that ProCARs are intratumorally activated by probiotically-delivered SAs, receive further stimulation from bacterial TLR agonists, and are safe and effective in multiple xenograft models. This approach repurposes tumor-colonizing bacteria as beacons that guide the activity of engineered T cells, and in turn builds the foundation for communities of living medicines.

bioengineering↗